Xinglong Pei
Papers
2
Total Citations
29
H-Index
2
About
Xinglong Pei is a rising researcher at the forefront of rehabilitation robotics and human–machine interaction, with a focus on intelligent control systems and biosignal processing. His work bridges the gap between uncertain robotic dynamics and practical assistive technologies, particularly for upper limb rehabilitation. Pei’s most cited paper, “Continuous estimation of upper limb joint angle from sEMG based on multiple decomposition feature and BiLSTM network” (2022, 23 citations), introduces a novel deep learning approach that extracts multi-domain features from surface electromyography signals to accurately predict joint motion—a critical step toward intuitive, non-invasive control of exoskeletons. In his more recent work, “Data-Driven Model-Free Adaptive Containment Control for Uncertain Rehabilitation Exoskeleton Robots with Input Constraints” (2024, 6 citations), Pei tackles the challenge of controlling exoskeletons with unknown dynamics and actuator saturation. He proposes a model-free adaptive containment control (MFACC) scheme that ensures stable, safe operation without requiring precise mathematical models of the robot or user. This data-driven methodology is particularly impactful for real-world rehabilitation, where patient variability and hardware constraints are unavoidable. Pei’s contributions are shaping the next generation of adaptive, user-friendly assistive robots, with potential applications in stroke recovery and mobility assistance.
Research Focus
Key Achievements
Top Papers
- 1
- 2